Mohammad Hadi Mohammadi

Bio: Mohammad Hadi Mohammadi is an academic researcher from Donghua University. The author has contributed to research in topics: Social media analytics & Hybrid neural network. The author has an hindex of 1, co-authored 3 publications receiving 8 citations.

Sentiment Analysis on Twitter: A text Mining Approach to the Afghanistan Status Reviews

Abstract: Twitter has become a popular social media network where people express their opinions and views on political and other topics. Social media analysis of Twitter can be used to understand which sentiment and opinions are implicit in these social media data. The purpose of this paper is to present an approach of natural language pre-processing, text mining, and sentiment analysis techniques to analyze Twitter data related to Afghanistan through a case study. Our article analyzes the Twitter English data about Afghanistan. The value of the proposed approach was to understand the most discomforts and happiness of people, their opinions, and the country situation in the different time through a case study. We found that from 29 March 2018 to 12 Jun 2018 almost always negative comments are higher than positives while from 13 Jun 2018 to 21 Jun 2018 it is just opposite, the positive comments are higher than negative comments on Twitter. The reason for this was the interim peace for a few days that had taken place between Afghan government and the Taliban terrorist group. The outcomes of this research can help the palpitations, companies, and stockholders to use social media network as a great information source for their better political strategies and better business decision-making for their current and future intentions. It provides a feasible approach and a case study as an example to assist the researchers to apply the sentiment techniques more effectively.

An Approach of Predicting Heart Disease Using a Hybrid Neural Network and Decision Tree

Abstract: Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.

Analisis perspektif pada penerapan e-money menggunakan delone and mclean is success model di bandara sultan syarif kasim ii pekanbaru

Abstract: The attendance system found in an institution or university uses an id card, finger print or using the manual method. This method still has many shortcomings such as signature forgery, loss of ID cards, queue up which can waste a lot of time. In this study, a QR CODE-based Online Attendance System with REST architecture was developed which resulted inweb and mobile-based applications, so that students can make attendance more practical and efficient with their gadgets, besides that lecturers can monitor data and student attendance graph on a web-based application. The attendance System was developed using the REST architecture because the architecture is language and platformagnostic, so it can be used by many programming languages and many platforms, and the REST architecture has a design and philosophy closer to the web, using the HTTP protocol.

Sentiment analysis on e-sports for education curriculum using naive bayes and support vector machine

Abstract: The development of e-sports education is not just playing games, but about start making, development, marketing, research and other forms education aimed at training skills and providing knowledge in fostering character The opinions expressed by the public can take form support, criticism and input Very large volume of comments need to be analyzed accurately in order separate positive and negative sentiments This research was conducted to measure opinions or separate positive and negative sentiments towards e-sports education, so that valuable information can be sought from social media Data used in this study was obtained by crawling on social media Twitter This study uses a classification algorithm, Naive Bayes and Support Vector Machine Comparison two algorithms produces predictions obtained that the Naive Bayes algorithm with SMOTE gets accuracy value 7032%, and AUC value 0954 While Support Vector Machine with SMOTE gets accuracy value 6692% and AUC value 0832 From these results can be concluded that Naive Bayes algorithm has a higher accuracy compared to Support Vector Machine algorithm, it can be seen that the accuracy difference between naive Bayes and the vector machine support is 34% Naive Bayes algorithm can thus better predict the achievement of e-sports for students' learning curriculum

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Abstract: Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Over a Decade of Social Opinion Mining: A Systematic Review

Abstract: Social media popularity and importance is on the increase due to people using it for various types of social interaction across multiple channels. This systematic review focuses on the evolving research area of Social Opinion Mining, tasked with the identification of multiple opinion dimensions, such as subjectivity, sentiment polarity, emotion, affect, sarcasm and irony, from user-generated content represented across multiple social media platforms and in various media formats, like text, image, video and audio. Through Social Opinion Mining, natural language can be understood in terms of the different opinion dimensions, as expressed by humans. This contributes towards the evolution of Artificial Intelligence which in turn helps the advancement of several real-world use cases, such as customer service and decision making. A thorough systematic review was carried out on Social Opinion Mining research which totals 485 published studies and spans a period of twelve years between 2007 and 2018. The in-depth analysis focuses on the social media platforms, techniques, social datasets, language, modality, tools and technologies, and other aspects derived. Social Opinion Mining can be utilised in many application areas, ranging from marketing, advertising and sales for product/service management, and in multiple domains and industries, such as politics, technology, finance, healthcare, sports and government. The latest developments in Social Opinion Mining beyond 2018 are also presented together with future research directions, with the aim of leaving a wider academic and societal impact in several real-world applications.